Cradle-to-Gate Life Cycle Assessment for Geopolymer Concrete¶

The purpose of this LCA is to

1- identify the most critical process (that has principal contribution in more impact categories) with less effort, less time and more accuracy, as compared to traditional stacked bar charts

2- Coalesce midpoint, endpoint LCA's, and process contributions in a visually appealing way to summarize LCA results (also usefull for posters and graphical abstracts of scientific publications)

In [1]:
import pandas as pd
import uuid
import math
from datetime import datetime
from matplotlib import pyplot as plt
import matplotlib.mlab as mlab
from matplotlib import rcParams
import matplotlib.patches as mpatches
import seaborn as sns
import plotly.express as px
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
import plotly.graph_objects as go
import pandas as pd
%matplotlib inline

Goal and Scope¶

The goal of this LCA is to identify the most critical process contributing to the environmental impacts of geopolymer concrete. The functional unit is 1m3 of concrete. The cradle-to-gate system boundary is selected and is presented in figure below.

In [2]:
from PIL import Image
img = Image.open('Sys Boundary.jpg')
img
Out[2]:

Life Cycle Inventory Analysis¶

In [3]:
geopolymer_data = pd.read_csv('geopolymer.csv')
print("The testing matrix has been taken from experimental work by us in the lab")
geopolymer_data

The testing matrix has been taken from experimental work by us in the lab
Out[3]:
in/out flow amount units
0 in Fly ash 480 Kg
1 in GGBFS 120 Kg
2 in Sand 600 Kg
3 in Gravel 900 Kg
4 in NaOH 80 Kg
5 in Sodium Silicate 160 Kg
6 in Mixing 2 kWh
7 out Geopolymer 1 m3
In [4]:
pip install -U olca-ipc

Requirement already satisfied: olca-ipc in c:\users\hp\anaconda3\lib\site-packages (0.0.12)
Requirement already satisfied: requests in c:\users\hp\anaconda3\lib\site-packages (from olca-ipc) (2.27.1)
Requirement already satisfied: charset-normalizer~=2.0.0 in c:\users\hp\anaconda3\lib\site-packages (from requests->olca-ipc) (2.0.4)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\hp\anaconda3\lib\site-packages (from requests->olca-ipc) (2021.10.8)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\users\hp\anaconda3\lib\site-packages (from requests->olca-ipc) (1.26.9)
Requirement already satisfied: idna<4,>=2.5 in c:\users\hp\anaconda3\lib\site-packages (from requests->olca-ipc) (3.3)
Note: you may need to restart the kernel to use updated packages.
In [5]:
cd C:\Users\HP\Downloads\olca-ipc.py-master

C:\Users\HP\Downloads\olca-ipc.py-master
In [6]:
import olca

params = {'mathtext.default': 'regular' }

client = olca.Client(8080)
client
Out[6]:
<olca.ipc.Client at 0x255e1b543d0>
In [7]:
dt_object = datetime.fromtimestamp(datetime.timestamp(datetime.now()))
In [8]:
#creating flows
volume = client.find(olca.FlowProperty, 'Volume')

geopolymer = olca.product_flow_of('geopolymer', volume)
geopolymer
geopolymer.description = '1 m3 Geopolymer concrete added via olca-ipc on %s.' % (dt_object)
client.insert(geopolymer)
Out[8]:
'ok'
In [9]:
geopolymer_data['flow'][0]
geopolymer
Out[9]:
{
  "@id": "63eb19b1-88ed-40c3-a4f4-8f65c81a28ab",
  "@type": "Flow",
  "description": "1 m3 Geopolymer concrete added via olca-ipc on 2023-01-08 01:11:56.159817.",
  "flowProperties": [
    {
      "@id": "",
      "@type": "FlowPropertyFactor",
      "conversionFactor": 1.0,
      "flowProperty": {
        "@id": "93a60a56-a3c8-22da-a746-0800200c9a66",
        "@type": "FlowProperty",
        "name": "Volume"
      },
      "referenceFlowProperty": true
    }
  ],
  "flowType": "PRODUCT_FLOW",
  "lastChange": "2023-01-07T20:11:56.608458Z",
  "name": "geopolymer",
  "version": "00.00.000"
}
In [10]:
mass = client.find(olca.FlowProperty, 'Mass')
x = [0,1,2,3,4,5]

for a in x:
    flow_name = geopolymer_data['flow'][a]
    print(flow_name)
    flow_name = olca.product_flow_of(geopolymer_data['flow'][a], volume)
    flow_name.description = 'flow for', geopolymer_data['flow'][a],'added via olca-ipc on %s.' % (dt_object)
    client.insert(flow_name) 
flow_name

Fly ash
GGBFS
Sand
Gravel
NaOH
Sodium Silicate
Out[10]:
{
  "@id": "1123a426-5ddd-4a5c-b8fa-07e30ed02318",
  "@type": "Flow",
  "description": [
    "flow for",
    "Sodium Silicate",
    "added via olca-ipc on 2023-01-08 01:11:56.159817."
  ],
  "flowProperties": [
    {
      "@id": "",
      "@type": "FlowPropertyFactor",
      "conversionFactor": 1.0,
      "flowProperty": {
        "@id": "93a60a56-a3c8-22da-a746-0800200c9a66",
        "@type": "FlowProperty",
        "name": "Volume"
      },
      "referenceFlowProperty": true
    }
  ],
  "flowType": "PRODUCT_FLOW",
  "lastChange": "2023-01-07T20:11:57.163476Z",
  "name": "Sodium Silicate",
  "version": "00.00.000"
}
In [11]:
#Creating Processes
dt_object = datetime.fromtimestamp(datetime.timestamp(datetime.now()))
x=[0,1,2,3,4,5,6,7]

for a in x:
    process_name = geopolymer_data['flow'][a]
    print(process_name)
    process_name = olca.process_of(geopolymer_data['flow'][a])
    process_name.description = 'Added via olca-ipc on %s.' % (dt_object)
    client.insert(process_name)
process_name
    

Fly ash
GGBFS
Sand
Gravel
NaOH
Sodium Silicate
Mixing
Geopolymer
Out[11]:
{
  "@id": "e734854c-6d46-4506-9750-b3867e95279a",
  "@type": "Process",
  "description": "Added via olca-ipc on 2023-01-08 01:11:57.205133.",
  "lastChange": "2023-01-07T20:11:57.466676Z",
  "name": "Geopolymer",
  "processType": "UNIT_PROCESS",
  "version": "00.00.000"
}
In [12]:
#adding input/output flows in the geopolymer process 

target_refs = []

for a in x:
    all_obj = client.get_descriptors(olca.Flow)
    cache = [obj for obj in all_obj if geopolymer_data['flow'][a] == obj.name]
    target_refs.append(cache)
In [13]:
process_descriptor = client.get_descriptors(olca.Process)
process_list = []
id_list = []

for process in process_descriptor:
    process_list.append(process.name)
    id_list.append(process.id)

processes_df = pd.DataFrame(list(zip(process_list,
                                   id_list)),
                            columns=['name', 'id'])
processes_df
Out[13]:
name id
0 heat and power co-generation, biogas, gas engi... 16f38842-59a3-3a40-a49b-f23b20175b0b
1 heat and power co-generation, biogas, gas engi... be8cc65b-d612-3dea-84c4-00a43d0922ed
2 market for land tenure, arable land, measured ... e3f589d2-dbe7-32f1-accb-19de6b323b5c
3 fluorescent whitening agent production, distyr... 6e57e177-1a06-36bb-aad8-b79c7bc61105
4 electricity voltage transformation from medium... 7bba9abd-7e6a-36bc-97f5-93432dcfb42f
... ... ...
19579 Gravel 8345f6eb-49e9-4713-8baa-6b3135965384
19580 NaOH fd4fc39f-52ed-457a-a8f4-e885ea4405ff
19581 Sodium Silicate 618cd595-5855-4b93-a4ea-9a3f8c3dab37
19582 Mixing 828a510b-0975-4575-9e01-46cbdd9e2298
19583 Geopolymer e734854c-6d46-4506-9750-b3867e95279a

19584 rows × 2 columns

In [14]:
#creating product systems
product_system = client.create_product_system(processes_df['id'][processes_df.last_valid_index()],
                                              default_providers='prefer',
                                              preferred_type='UNIT_PROCESS')
In [15]:
psID = product_system.id
psID
Out[15]:
'6ad0ce33-2292-4716-8a02-7580b6b1328d'

Life Cycle Impact Assessments¶

The LCIA can be performed by clicking "quick calculations" button in OpenLCA, available on the product system that has been created with jupyter

In [16]:
cd

C:\Users\HP
In [17]:
LCIA_results= pd.read_csv('LCIA.csv')
LCIA_results
Out[17]:
Impact Category Unit Value Ecosystem Human Health Resources
0 Climate Change kg CO2-Eq 121.98000 36.594000 42.693000 28.055400
1 Ozone Depletion kg CFC-11-Eq 0.00005 0.000006 0.000017 0.000016
2 Particulate Matter Formation kg PM10-Eq 0.15340 0.030700 0.049088 0.000000
3 Acidification kg SO2-Eq 0.95800 0.287400 0.000000 0.000000
4 Eutrophication kg P-Eq 2.77900 1.111600 0.833700 0.000000
In [18]:
process_contributions= pd.read_csv('process_contribution.csv')
process_contributions
Out[18]:
Flow GHG Ozone Depeltion Particulate Matter Acidification Eutrophication
0 Gravel 6.50 1.12 4 0.50 1
1 Sand 2.32 1.00 2 0.30 2
2 Fly Ash 11.97 2.12 84 63.00 11
3 GGBFS 15.17 40.42 6 13.50 9
4 SS 25.66 25.00 1 1.04 45
5 SH 38.00 29.00 1 20.80 32

Interpretation/Visualization¶

Visualization 1:

This visualization indicates the most critical process (that has prinicpal contribution in more impact categories)

The traditional stacked bar chats used in LCA visualization are intricate to analyze the most critical product and more often than not, the conclusions drawn are not accuate. For example, previously it was concluded that activators are the most critical process in impacts of geopolymer concrete, whereas this visualization indicates that both activators and fly ash are principal contributors in 3 imapct categoies, and therefore both should be deemed as critical processes

In [19]:
label = ["Activator", "GGBFS", "Fly Ash",  "Activator", "GGBFS", "Fly Ash",  "Activator", "Fly Ash", "GGBFS", "Fly Ash", "GGBFS", "Activator", "Fly Ash", "Activator", "GGBFS", "Activator", "Fly Ash", "GGBFS"]
source = [0,0,0, 1,1,1, 2,2,2, 3,3,3, 4,4,4, 5,5,5, 6,6,6, 7,7,7, 8,8,8, 9,9,9, 10,10,10, 11,11,11, 12,12,12, 13,13,13, 14,14,14]  #3 source nodes
target = [3,4,5, 3,4,5, 3,4,5, 6,7,8, 6,7,8, 6,7,8, 9,10,11, 9,10,11,  9,10,11, 12,13,14, 12,13,14, 12,13,14, 15,16,17, 15,16,17, 15,16,17]  # 4 target nodes 
value = [1,0,0, 0,1,0, 0,0,1, 1,0,0, 0,0,1, 0,1,0, 0,0,1, 1,0,0, 0,1,0, 1,0,0, 0,0,1, 0,1,0, 0,1,0, 1,0,0, 0,0,1]
link = dict(source=source, target=target, value=value, color = ['rgba(230, 126, 34,0.5)','rgba(230, 126, 34,0.5)','rgba(230, 126, 34,0.5)','rgba(34, 153, 84,0.5)','rgba(34, 153, 84,0.5)','rgba(34, 153, 84,0.5)','rgba(52, 152, 219,0.5)','rgba(52, 152, 219,0.5)','rgba(52, 152, 219,0.5)', 'rgba(230, 126, 34,0.5)','rgba(230, 126, 34,0.5)','rgba(230, 126, 34,0.5)','rgba(34, 153, 84,0.5)','rgba(34, 153, 84,0.5)','rgba(34, 153, 84,0.5)','rgba(52, 152, 219,0.5)','rgba(52, 152, 219,0.5)','rgba(52, 152, 219,0.5)',
          'rgba(230, 126, 34,0.5)','rgba(230, 126, 34,0.5)','rgba(230, 126, 34,0.5)','rgba(52, 152, 219,0.5)','rgba(52, 152, 219,0.5)','rgba(52, 152, 219,0.5)','rgba(34, 153, 84,0.5)','rgba(34, 153, 84,0.5)','rgba(34, 153, 84,0.5)', 'rgba(52, 152, 219,0.5)','rgba(52, 152, 219,0.5)','rgba(52, 152, 219,0.5)','rgba(34, 153, 84,0.5)','rgba(34, 153, 84,0.5)','rgba(34, 153, 84,0.5)','rgba(230, 126, 34,0.5)','rgba(230, 126, 34,0.5)','rgba(230, 126, 34,0.5)',
         'rgba(52, 152, 219,0.5)','rgba(52, 152, 219,0.5)','rgba(52, 152, 219,0.5)','rgba(230, 126, 34,0.5)','rgba(230, 126, 34,0.5)','rgba(230, 126, 34,0.5)','rgba(34, 153, 84,0.5)','rgba(34, 153, 84,0.5)','rgba(34, 153, 84,0.5)', 'rgba(230, 126, 34,0.5)','rgba(230, 126, 34,0.5)','rgba(230, 126, 34,0.5)','rgba(52, 152, 219,0.5)','rgba(52, 152, 219,0.5)','rgba(52, 152, 219,0.5)','rgba(34, 153, 84,0.5)','rgba(34, 153, 84,0.5)','rgba(34, 153, 84,0.5)'],)
node = dict(label=label, x=[0.1,0.1,0.1, 0.25,0.25,0.25, 0.4,0.4,0.4, 0.55,0.55,0.55, 0.7,0.7,0.7, 0.85,0.85,0.85], y= [0.1,0.5,0.9, 0.9,0.5,0.1, 0.1,0.5,0.9, 0.1,0.5,0.9, 0.1,0.5,0.9, 0.1,0.5,0.9] ,pad = 0, thickness = 60, color = ['#FF7F50','#2ECC71','#6495ED', '#FF7F50','#2ECC71','#6495ED', '#FF7F50','#6495ED','#2ECC71', '#6495ED','#2ECC71','#FF7F50', '#6495ED','#FF7F50','#2ECC71', '#FF7F50','#6495ED','#2ECC71'])
data= go.Sankey(link=link, node=node)
fig = go.Figure(data)
fig.update_layout(
    hovermode = 'x',
    font=dict(size = 10, color = 'black')
)
fig.add_annotation(
    x= 0.1,
    y=1.065,
    text = "Climate Change",
    font=dict(size = 12, color = 'black')
)
fig.add_annotation(
    x= 0.25,
    y=1.065,
    text = "Ionizing Radiation",
    font=dict(size = 12, color = 'black')
)
fig.add_annotation(
    x= 0.4,
    y=1.065,
    text = "Ozone Depletion",
    font=dict(size = 12, color = 'black')
)
fig.add_annotation(
    x= 0.55,
    y=1.065,
    text = "Particulate Matter",
    font=dict(size = 12, color = 'black')
)
fig.add_annotation(
    x= 0.7,
    y=1.065,
    text = "Acidification",
    font=dict(size = 12, color = 'black')
)
fig.add_annotation(
    x= 0.85,
    y=1.065,
    text = "Eutrophication",
    font=dict(size = 12, color = 'black')
)

fig.show()

Visualization 2:

This visualization coalesce midpoint lca, endpoint lca and process contributions in a visually appealing way to summarize lca in one figure (also usefull in posters and graphical abstracts of scientific publications)

In [20]:
label = ["Gravel", "Sand", "Fly ash", "GGBFS", "Sodium Silicate", "Sodium Hydroxide", "Climate Change", "Ozone Depletion", "Particulate Matter Formation", "Acidification", "Ecosystem Quality", "Human Health", "Resources"]
source = [0,0,0,0, 1,1,1,1, 2,2,2,2, 3,3,3,3, 4,4,4,4, 5,5,5,5, 6,6,6, 7,7,7, 8,8,8, 9,9,9]  #3 source nodes
target = [6,7,8,9, 6,7,8,9, 6,7,8,9, 6,7,8,9, 6,7,8,9,  6,7,8,9, 10,11,12, 10,11,12, 10,11,12, 10,11,12 ]  # 4 target nodes 
value = [6,2,4,0.5, 3,1,2,0.3, 12,3,84,63, 15,40,6,13, 26,25,1,1, 38,29,1,20, 30,35,23, 12,35,32, 20,32,0, 30,0,0]
link = dict(source=source, target=target, value=value)
node = dict(label=label, pad = 35, thickness = 50)
data= go.Sankey(link=link, node=node)
In [21]:
fig = go.Figure(data)
fig.update_layout(
    hovermode = 'x',
    font=dict(size = 12, color = 'black')
)
fig.add_annotation(
    x= 0.03,
    y=1,
    text = "Processes",
    font=dict(size = 15, color = 'black')
)
fig.add_annotation(
    x= 0.5,
    y=0.82,
    text = "Midpoint LCA",
    font=dict(size = 15, color = 'black')
)
fig.add_annotation(
    x= 0.97,
    y=0.62,
    text = "Endpoint LCA",
    font=dict(size = 15, color = 'black')
)
fig.show()

Acknowledgments¶

I would like to thank Mr. Julian Rickert for his amazing tutorials in integrating open LCA and Jupyter Notebooks

I am gratefull to open LCA for providing open source software license

I thank ecoinvent for providing academic license of ecoinvent database